code: def forward(
      ctx,
      decoder_states,
      encoder_out,
      encoder_padding_mask,
      parameters,
      config,
      cache,
  ):
      cuda_module = transformer_cuda_module
      forward_func = (
          cuda_module.transformer_decoder_layer_fw_fp16
          if config.fp16
          else cuda_module.transformer_decoder_layer_fw_fp32
      )
  
      (output,) = forward_func(
          config.layer_id,
          decoder_states,
          encoder_out,
          encoder_padding_mask,
          config.training,
          config.pre_layer_norm,
          config.quant_mode,
          cache,
      )
  
      if config.is_grad_enabled and config.training:
          ctx.save_for_backward(
              output,
              decoder_states,
              encoder_out,
              encoder_padding_mask,
          )
          ctx.config = config
      return output
  
imports: from itertools import zip_longest
  import math
  from dataclasses import dataclass
  import copy
  import torch
  from torch import nn
  from torch.autograd import Function
  from lightseq.training.ops.pytorch import TransformerBuilder
  from lightseq.training.ops.pytorch.builder import TransformerBuilder
  from lightseq.training.ops.pytorch.util import copy_para, state_dict, MODEL_ARCH, check_config, calc_offset
  from lightseq.training.ops.pytorch.quantization import weight_quant_config, act_quant_config, relu_quant_config
  from lightseq.training.ops.pytorch.layer_base import TransformerDecoderLayerBase
label_desc: Use image node_id 1 for calling the LSTransformerDecoderFunc obj's underlying member method code with example usage: obj.forward(ctx, decoder_states, encoder_out, encoder_padding_mask, parameters, config, cache) and returns: output